Made with SUS304 stainless steel. The edges of mufflers are tapered and burnt as if they were really used.
Made with SUS304 stainless steel. The edge of muffler is tapered and burnt as if it was really used.
See comparison of sizes and example of mounting.
Works good for protecting body from scratches by pins.
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Revised edition: Previously published as Game Changer, this edition of Raine Falling includes editorial revisions.
I really enjoyed Raine Falling.
The whole book was captivating from start to finish. It wasn't your usual MC read either, there was no club business or rival wars, this was the lives of two sisters and survival.
Told in third person in each persons pov mostly Raine's, this isn't my favorite choice in how I read books, I do like getting in the heads of the characters and like alternate first persons pov's but somehow with this book I didn't mind, it felt right it being told like a story.
It all begins when Raine, on the same night she was supposed to be celebrating her nursing degree went home to find her sister surrounded by bikers and her sisters brother beat up. Claire was addicted to cocaine & her boyfriend and he was addicted to drugs and they owed money to the wrong people.
Raine was given 5 days to come up with the cash.
Both sisters were brought up by a man who was their fathers best friend who was also in love with their mother, but he also saw the love his best friend had for her too, Claire and Raine's mother was a beautiful woman inside and out and the author did a beautiful job of letting me as a reader completely capture how she was, what took place and how Raine's dad had been so in love with her he was broken and heartbroken following her death, Prosper stepped in to look after the girls.
Prosper was the president of an MC and before he left Raine he had told her that no matter or when if she ever needed him he would be there and he gave her instructions.
The night Raine found her sister, she planned to get the money they owed but after another visit from a biker 2 days later she was in despair, gets her sister into rehab and used the instructions Prosper had given her which then leads her to the MC.
This is one of those story's that tells it as it is, from beginning to end with no misgivings along the way, its a beautiful journey, with small words of treasures that capture your heart, I trusted the author to deliver a hea and was not disappointed.
There was danger and angst along the way, hot moments which could of been more graphic but the whole book was balanced brilliantly, yes there was room for improvement with how some things were delivered but I was so caught up In the story and what was going to happen next I didn't care.
A story of survival, family and more importantly new beginnings which out of chaos Raine finds with Diego.
The characters I loved, although at times Diego made my blood boil for how he treated Raine but the story told of life's up and downs, danger, moving forward, regrets and love, the love of a man the love of a woman and the love of the family around you whatever shape or form they come in.
Fans of Cat porter would also love this story.


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Coupons, coupons, coupons. Many retailers will let you use coupons or promotions on top of the tax-free savings, so check to see if the stores you're planning on visiting have any special promotions or coupons available.
Don't buy just because you can. It can be tempting to buy something just because it's a good deal, but that doesn't always mean it's a good choice. To avoid this common mistake, make a list of all the items you need and budget out how much you'll need to purchase these essentials. Use an app like My Finance Tool in Online Banking to keep you accountable and aware of how much you're spending.
Watch the register. Keep an eye on the register to make sure you're receiving your tax-free discount. Nothing's worse than getting home and realizing none of your discounts have been applied.
Plan ahead. To make the most out of your tax-free savings, try to plan ahead and think of things you may need in the future. Do your children always lose their socks or go through notebooks like there's no tomorrow? Tax-free weekend is the perfect time to stock up.
Before you break out your shopping list and credit card, it's important to make sure you have a plan in place. If you follow these tips and shop smartly, you'll be making the most out of your tax-free savings!


text:
\section{Introduction}
\noindent With the rapid development of multimedia data on the Internet, multimodal summarization is a research direction worthy of attention and has broad development prospects. Therefore, some researches (\citealp{11zhu2018msmo,6li2020vmsmo,10zhu2020multimodal}) focus on studying multimodal summarization with multimodal output (MSMO) to help readers improve reading efficiency and satisfaction. However, these approaches abstract the summary from the raw data directly, which is ineffective in learning the latent and vital information from both the text and image content.

\begin{figure}

    \begin{center}
    \includegraphics[scale=0.24]{img/figure1_v7.pdf}
    \end{center}
    \caption{An example of the cross-modality semantics correlation in multimodal data.}
    \label{fig:f1}
\end{figure}

Usually, the visual image and text article have heterogeneous structures. Directly mapping visual input and textual input as global vectors (\citealp{11zhu2018msmo,6li2020vmsmo,10zhu2020multimodal}) is not effective to learn the important information for both modalities from each other, and even noisy information is added to decrease the performance of summarization. Previous experiments (\citealp{11zhu2018msmo}) have shown that multimodal input models may decrease summarization metric scores compared to text-only input models. Our experiments also show that some multimodal input model methods perform worse than traditional text-only input models. Therefore, one of the core problems of MSMO is how to effectively learn from each other modalities of data to obtain high-quality summaries.

Meanwhile, the correlation between the visual content and text article presents unique characteristics, providing clues to learning the important information from the two modalities complementarily to improve MSMO. As shown in Figure 1, the low-level objects in an image constitute the high-level semantics called scenes through the interaction between them. Therefore, by analyzing the objects and hence the scenes, we can know what the image describes from different levels. In the other data space, words are also the basic textual information in an article, while the combination of words, called sentences, present more abstract semantics information. Besides the intra-modal correlation, the semantics objects in the image and article are correlated in different levels. For example, in Figure \ref{fig:f1}, each person in the image may be related to a name in the article, and the football sport in the image is also described by a sentence in the article. By learning the inter-modal correlation, it can be known what is the important information in both of the modalities. Moreover, even there are some incomplete descriptions in one modality, it can be learned from the other modality by exploiting the inter-modal correlation. As shown in Figure \ref{fig:f1}, we can generate the more complete information about the relation between the player "Mascherano" and the event "block Robben's shot". Therefore, by exploiting the hierarchical cross-modality correlation, we can extract the important information from both image and article more effectively.

However, there is still a great challenge to learn the hierarchical cross-modality correlation. First, different modalities have different feature spaces and structures among the elements. It is nontrivial to learn an effective representation to reflect both the different content and structure information. Second, much noisy information might exist, while some important information might be missed in one of the modalities. However, there is no explicit knowledge about the correlation between different modalities of data.

To tackle the challenge, we propose a novel Hierarchical Cross-Modality Semantic Correlation Learning model (HCSCL) to learn the intra- and inter-modality correlation for MSMO. In particular, two modality encoders are proposed to learn the intra-modal correlation for image and article, respectively. Then, a hierarchical fusion framework is proposed to learn the hierarchical correlation between image and article. A hierarchical attention method is proposed to combine the different levels of features learned by the hierarchical fusion framework to generate the summary. Furthermore, we construct a new dataset\footnote{https://github.com/LitianD/HCSCL-MSDataset/} with relevant image annotation to provide the supervision information for the learning procedure. Extensive experiments on the dataset show that HCSCL significantly outperforms the baseline methods in automatic summarization metrics and fine-grained diversity tests.
Our main contributions are as follows:

\begin{itemize}
\item   We propose a hierarchical learning model HCSCL to learn the intra- and inter-modality correlation in the multimodal data. To the best of our knowledge, this is the first work that guides multimodal summarization by exploiting the fine-grained semantics and their correlation information inside the multimodal data.
\item We propose a multimodal visual graph learning method to capture the structure and the content information and reinforce the inter-modality interaction.
\item We construct a large-scale multimodal summarization dataset with relevant image annotations and object labels to evaluate the performance of MSMO.
\end{itemize}

\begin{figure*}[htbp]
    \begin{center}
    \includegraphics[scale=0.46]{img/figure2_v4.pdf}
    \end{center}
    \caption{(a) is the overview of HCSCL. It consists of three parts: Modality Feature Encoder, Hierarchical Semantic Relation Fusion, and Multimodal Output Summarizer. (b) is the Cross Modality Encoder used for hierarchical fusion. (c) is the Image Graph Encoder to connect relevant objects into scenes.}
    \label{fig:f2}
\end{figure*}

\section{Related works}
\textbf{Multimodal Summarization with Text Only Output.} Unlike text summarization (\citealp{zhang2018neural}; \citealp{xiao2019extractive}; \citealp{see2017get}; \citealp{gao2019abstractive}), multimodal summarization (\citealp{1uzzaman2011multimodal}) is defined as a task to generate a condensed summary from a multimodal input, such as text, image, and video. Several works focus on generating a better text summary with the help of images (\citealp{3chen2018abstractive}; \citealp{12li2018multi}, \citeyear{9li2020multimodal}). \citet{4palaskar2019multimodal} first release the How2 dataset for open-domain multimodal summarization. \citet{8liu2020multistage} focus on reducing noise in longer text and video summarization and propose a forget gate method to control redundant information. \citet{2li2017multi} and \citet{7khullar2020mast} aim to generate a text summary from more than three modalities: text-video-audio or text-image-audio-video.

\noindent\textbf{Multimodal Summarization with Multimodal Output.} Some other works generate a summarization containing both textual and visual content. \citet{11zhu2018msmo} first propose to output a text summary and select the most relevant image. Then \citet{10zhu2020multimodal} add a multimodal loss function to improve the relevance between text summaries and images. Besides, \citet{6li2020vmsmo} and \citet{5fu2020multi} fuse the text-video feature and generate a text summary with a significant image from its associated video. Multimodal output summarization can facilitate readers to obtain crucial information efficiently. Most of these approaches fuse the different modalities of data directly, which neglects the latent correlation and heterogeneity of internal structure among them. Therefore, their performance of summarization is affected and even worse than text summarization.

\section{Problem Formulation}
Our HCSCL model takes a long article and the associated images as the input and generates a summarization containing both the textual summary and the most representative image. $X^t = \left\{ S_1,S_2,...,S_n  \right\}$ is used to denote the textual input, which consists of $n$ sentences, and $X^v = \left\{ I_1,I_2,...,I_p  \right\}$ is used to denote the visual input associated $p$ images. $S_i = \left\{ x_{i,1},x_{i,2},...,x_{i,m} \right\}$ means the sentence $S_i$ has $m$ words. The summarization can be divided into text output and visual output. The textual output is denoted as a sequence of words $Y^t = \left\{ y_1,y_2,...,y_t  \right\}$, and the visual output is $Y^v = \left\{I  \right\}$. In order to generate a multimodal summarization, the model can be formulated as an optimization problem as follows:

$$
\arg\max_{\theta} HCSCL(Y^t,Y^v|X^t,X^v;\theta)
$$

\noindent where $\theta$ is the set of trainable parameters in the model.

\section{Model}
The current multimodal summarization methods have two drawbacks: 1) They neglect the heterogeneity of internal structure between visual content and text. 2) They mainly consider the visual content as the whole, which ignores the hierarchical correlation between different modalities of data.

Therefore, in this work, we propose a hierarchical cross-modality semantic correlation learning model, as shown in Figure \ref{fig:f2}, to improve multimodal summarization. HCSCL comprises three modules: the Modality Feature Encoder is used to encode each modality, the Hierarchical Semantic Correlation Fusion module is used to learn the hierarchical intra- and inter-modality correlation, and the Multimodal Output Summarizer is used to generate the multimodal summary by exploiting the hierarchical correlation. Table 1 presents the key variables used in different modules.

\begin{table}[!h]
\resizebox{84mm}{15mm}{
\centering

\begin{tabular}{p{23mm}ll}
\toprule
                                          & Variable & Description \\\hline
\multirow{2}{*}{\tabincell{l}{Modality Feature \\Encoder}} & $h^0_{i,j}$& The $j^{th}$ word embedding in the $i^{th}$ sentence\\
                                          & $v^0_{i}$  & The $i^{th}$ object feature for the article\\\hline
\multirow{2}{*}{\tabincell{l}{Word-object \\Fusion}}       & $h^1_{i,j}$& The $j^{th}$ word embedding fused with object feature\\
                                          & $v^1_{i}$  & The $i^{th}$ object feature fused with word embedding\\\hline
\multirow{4}{*}{\tabincell{l}{Sentence-scene \\Fusion}}    & $h^2_{i}$  & The $i^{th}$ sentence embedding for the article\\
                                          & $v^2_{i}$  & The $i^{th}$ scene feature for the article\\
                                          & $h^3_{i}$  & The $i^{th}$ sentence embedding fused with scene feature\\
                                          & $v^3_{i}$  & The $i^{th}$ scene feature fused with sentence embedding\\

\bottomrule
\end{tabular}
}
\caption{Description of key variables.}
\label{table:variable}
\end{table}

\subsection{Modality Feature Encoder}

\quad \textbf{Text Encoder.} We employ LSTM to encode an input article to a vector representation. Specifically, given an input article $X^t=\{S_1, S_2, \cdots, S_n\}$, the embedding of the words in the $i^{th}$ sentence $S_i = \{x_{i,1}, \cdots, x_{i,m}\}$ is learned as follows:\\
\begin{equation}
   \stackrel{\longrightarrow} {h^0_{i,j}} = LSTM(E[x_{i,j}], \stackrel{\longrightarrow}{h^0_{i-1,j}})
\end{equation}
\begin{equation}
    \stackrel{\longleftarrow}{h^0_{i,j}} = LSTM(E[x_{i,j}], \stackrel{\longleftarrow}{h^0_{i+1,j}})
\end{equation}
where $E[x_{i,j}]$ is the embedding vector of word $x_{i,j}$. $h^0_{i,j}=[\stackrel{\longrightarrow}{h^0_{i,j}}; \stackrel{\longleftarrow}{h^0_{i,j}}]$ represents the hidden state of the $j^{th}$ word in the $i^{th}$ sentence. The $i^{th}$ sentence's word embedding is $\left\{{h^0_{i,1},h^0_{i,1},...,h^0_{i,m}}\right\}$.

\ \textbf{Image Encoder.} Given a set of images $X^v=\{I_1, I_2, \cdots, I_p\}$ as the visual input, we apply a Faster R-CNN (\citealp{ren2016faster}) initialized with ResNet-101 (\citealp{he2016deep}) to obtain object proposals for each image. For each object proposal, a triple set $(o_i, l_i, a_i)$ is extracted. $o_i$ is a feature vector extracted from the region of interest (ROI) pooling layer in the Region Proposal Network. $l_i$ is a 4-dimensional bounding box location representation. $a_i$ is a one-hot attribute class feature. Then we use the triple set to form a representation of the $i^{th}$ object as follows:
\begin{equation}
    v_i^0 = Concat(o_i, W^{l}l_i, W^{a}a_i),
\end{equation}
where $W^{l}$ and $W^{a}$ are trainable embedding matrices. $\left\{v_1^0,v_2^0,...,v_q^0\right\}$ is a set of $q$ objects' features.

\subsection{Hierarchical Semantic Correlation Fusion}

Most existing approaches embed the whole image as a global vector, ignoring the internal and cross-modal correlation in the multimodal data. It is not effective to integrate the important information from different modalities. We propose a hierarchical semantic correlation fusion module, which can capture the hierarchical intra- and inter-modal correlation. It learns the important information in each modality by exploiting the intra-modal correlation at different grains and hence learns the important information in the multimodal content by exploiting the inter-modal correlation to reinforce each other, as Figure \ref{fig:f2} shows. This module learns the correlation in two levels: word-object fusion and sentence-scene fusion. The word-object fusion component learns the correlation between words and objects to add visual information into the text entity features. In the sentence-scene fusion component, a visual relation graph is built on the objects to form a sub-scene graph fused with the sentences to learn the inter-modal correlation. Notably, an attention-based cross-modality encoder (CME) (\citealp{tan2019lxmert}) is employed to learn the inter-modality correlation, which enhances the critical information by each other.

\ \textbf{Word-object Fusion.} The framework of CME is shown in Figure \ref{fig:f2}(b). Instead of directly using the attention mechanism, we employ cross-modality attention to generate a fused representation for the multimodal content. CME consists of three parts: cross-attention layer, self-attention layer, and feed-forward layer.

To fuse the $i^{th}$ sentence's word embeddings $\left\{{h^0_{i,1},h^0_{i,1},...,h^0_{i,m}}\right\}$ and objects' features $\left\{v_1^0,v_2^0,...,v_q^0\right\}$, the cross-attention layer is defined as:
\begin{equation}
a_k = score(h^0_{i,j},v_k^0)
\end{equation}
\begin{equation}
\alpha_k = exp(a_k)/ \sum_qexp(a_k)
\end{equation}
\begin{equation}
CrossAtt_{h\rightarrow v} = \sum_q \alpha_kv_k^0
\end{equation}
where $h^0_{i,j}$ is a query word vector, $v_k^0$ is visual object vectors and $score$ is defined as product function to calculate the similarity value $a_i$. The three steps of CME is defined as follows:
\begin{equation}
h^{Cross}_{i,j} = CrossAtt_{h\rightarrow v}(h^0_{i,j},\left\{v_1^0,v_2^0,...,v_q^0 \right\})
\end{equation}
\begin{equation}
h^{Self}_{i,j} = SelfAtt_{h\rightarrow v}(h^{Cross}_{i,j},\left\{h^{Cross}_{i,j} \right\})\\
\end{equation}
\begin{equation}
\left\{h_{i,j}^{out}\right\}= FF({\left\{h^{Self}_{i,1},h^{Self}_{i,2},...,h^{Self}_{i,m} \right\}})
\end{equation}
where $h^{Cross}_{i,j}$ is the result after cross-attention layer, $h^{Self}_{i,j}$ is the result after self-attention and $FF(*)$ is the feed-forward layers. Residual connection and layer normalization are also added after each sub-layer. Three steps are repeated $N_x$ times.

The inter-modal correlation between the textual words and the visual objects is learned by cross-modality attention. The learned multimodal representation is as follows:
\begin{equation}
h^1_{i,j} = CME(h^0_{i,j},\left\{v_1^0,v_2^0,...,v_q^0\right\})
\end{equation}
\begin{equation}
v^1_{i} = CME(v_{q}^0,\left\{h^0_{i,1},h^0_{i,2}...,h^0_{i,m}\right\})
\end{equation}
where $h^1_{i,j}$ is the representation of the $j^{th}$ word in the $i^{th}$ sentence after the fusing procedure, and $v^1_{i}$ is the fused representation of the $i^{th}$ object. $\left\{h^1_{i,1},h^1_{i,2},...,h^1_{i,m}\right\}$ is the word embeddings fused with object features and  $\left\{v_1^1,v_2^1,...,v_q^1\right\}$ is the object features fused with word vectors.

\ \textbf{Sentence-scene Fusion.} After obtaining the fused word features, for each sentence, we use LSTM to obtain a representation of the entire sentence. Then, the sentence representations are further fused with the visual feature by exploiting the correlation. Specifically, the representation of the $i^{th}$ sentence is computed as follows:
\begin{equation}
    h^2_i=LSTM(h^1_{i,1},h^1_{i,2},...,h^1_{i,m}).
\end{equation}

On the other side, a part of objects in an image is correlated, forming a scene to denote a more abstract concept or activity. The scenes are critical components of a summary. We propose an Image Graph Encoder to learn the scene representation, which can capture both the structure and the content information, as shown in Figure \ref{fig:f2}(c). First, based on the bounding box extracted by the image encoder, an Intersection over Union (IOU) score is calculated for every two objects. Next, a relation graph with adjacency matrix $A$ is constructed, where $A_{ij}=1$ if the IOU score exceeds the threshold value and $A_{ij}=0$ else.

Then, we calculate the edge weight as follows. Given two target node feature $v^1_i$ and $v^1_j$, the feature score $s^{feature}_{ij}$ from node $i$ to $j$ is first calculated:

\begin{equation}
s^{feature}_{ij} = w_1^T\sigma(w_2\cdot Concat(v^1_i,v^1_j))
\end{equation}
where $w_1$ and $w_2$ are learned parameters, $\sigma$ is the activation function. For every directed edge of node $i$, we apply a softmax function over the IOU score $s_{ij}^{IOU}$ and feature score $s^{feature}_{ij}$ to obtain the edge weight $s^{edge}_{ij}$:

\begin{equation}
s^{edge}_{ij} = exp(s_{ij}^{IOU}\cdot s^{feature}_{ij})/ \sum_{t\in N(i)}exp(s_{it}^{IOU}\cdot s^{feature}_{it})
\end{equation}
where $N(i)$ is the neighbors of $i$. Next, node feature is updated and combined with the connected node feature to formulate a sub-scene graph representation:

\begin{equation}
\tilde{v}^1_i = \sigma(v^1_i+\sum_{j\in N(i)}s^{edge}_{ij} A_{ij} w_3 v^1_j)
\end{equation}
\begin{equation}
\left\{ v_p^2 \right\} = readout(\tilde{v}^1_1,\tilde{v}^1_2,...,\tilde{v}^1_q)
\end{equation}
where $w_3$ is the learned parameters, $readout$ is a node value aggregation function used to combine nodes' vectors to generate sub-scene graph vectors. $\left\{v^2_1,v^2_2,...,v^2_p\right\}$ is a set of $p$ scene vectors. We define the readout function as:

\begin{equation}
    \left\{ v^2_p \right\} = \frac{1}{N}\sum_{i\in N(i)}\tilde{v}^1_i+Maxpooling({\tilde{v}_1^1,\tilde{v}_2^1,...,\tilde{v}_q^1})
\end{equation}

Finally, the sentence-scene fused features are generated by CME as follows:
\begin{equation}
    h^3_{i} = CME(h_i^2,\left\{v^2_1,v^2_2,...,v^2_p\right\}),
\end{equation}
\begin{equation}
    v^3_{i} = CME(v^2_{i},\left\{h^2_{1},h^2_{2},...,h^2_{n}\right\}).
\end{equation}
where $h^3_{i}$ is the representation of the $i^{th}$ sentence after the fusing procedure, and $v^3_{i}$ is the representation of the $i^{th}$ scene. $\left\{h^3_{1},h^3_{2},...,h^3_{n}\right\}$ is the set of sentence embedding fused with scene graph feature and  $\left\{v_1^3,v_2^3,...,v_p^3\right\}$ is the set of scene graph feature fused with sentence feature.

\subsection{Multimodal Output Summarizer}
The summarizer generates a text summary associated with the most relevant image. In the text summary generation, hierarchical attention is built to combine both word and sentence features. In the image selector,  an object-scene gate mechanism is proposed to select an image as the visual output.

\ \textbf{Text Summary Generation.} In the hierarchical fusion module discussed above, visual objects and scenes features are respectively fused into words and sentences. In this summarizer, a hierarchical attention method is proposed to exploit the two levels of correlation in the decoder stage.

First, LSTM is used to decode sentence features, and the hidden state $h^{\prime}_i$ is generated as follows:

\begin{equation}
    \left\{h^{\prime}_{1},h^{\prime}_{2},...,h^{\prime}_{t}\right\}=LSTM(h^3_{1},h^3_{2},...,h^3_{n})
\end{equation}

For the sentence level, the $i^{th}$ sentence weight $\beta^{sent}_i$ is calculated as follows:

\begin{equation}
    \beta^{sent}_i = \mathop{softmax}\limits_{i}(score(h^3_{i},h^{\prime}_{t-1}))
\end{equation}

For the word level, the weight $\beta^{word}_{i,j}$ of the $j^{th}$ word in the $i^{th}$ sentence is calculated as follows:

\begin{equation}
    \beta^{word}_{i,j} = \mathop{softmax}\limits_{i,j}(\beta^{sent}_{i} \cdot  score(h^1_{i,j},h^{\prime}_{t-1}))
\end{equation}
where $h^3_{i}$ and $h^1_{i,j}$ are sentence and word features respectively. Then the context vector $c_t$ at timestep $t$ and the word probability of $y_t$ are calculated as follows:

\begin{equation}
    c_t = \sum_{i=1}^N\sum_{j=1}^M\beta^{word}_{i,j}h^1_{i,j}
\end{equation}
\begin{equation}
    p(y_t|y_{1:t-1}) = softmax(V^T  FF(h^{\prime}_t,c_t))
\end{equation}
where $V$ is the vocabulary weight matrix. The total loss of textual summary is calculated as follows:
\begin{equation}
    L_{text} = -\sum_{t}\log{p(y_t)}
\end{equation}

\ \textbf{Image Selector.} The most relevant image should match the summary semantic in both the object level and the scene level. Therefore, the summary image is selected based on both the object features and the scenes graph features. First, the hidden state features $h^{\prime}_t$ are used to calculate the relevance score with object features $v^1_i$ and scene features $v^3_j$. Then, an object-scene gate mechanism is proposed to calculate the weights of the two types of features. The score of image $I$ is calculated as follows:
\begin{equation}
    s^{obj}_i = \sigma(FF(v^1_i h^{\prime}_t))
\end{equation}
\begin{equation}
    s^{scene}_j = \sigma(FF(v^3_j h^{\prime}_t))
\end{equation}
\begin{equation}
    \lambda^{*} = \sigma(FF(h^{\prime}_t))
\end{equation}
\begin{equation}
    s^{image}_I = \lambda^{*} \sum_{i \in I} s^{obj}_i+(1-\lambda^{*})\sum_{j \in I}s^{scene}_j
\end{equation}
where $\sum_{i \in I} s^{obj}_i$ is the sum of all scores of the object features in image $I$, $\sum_{j \in I}s^{scene}_j$ is the sum of all scores of the scene graph features in image $I$, and $\lambda^{*}$ is a balance weight between them. The image with the highest score is considered as the image output. The image summary loss is calculated as follows:

\begin{equation}
    L_{image} = -\log({softmax(s^{image}_{I})})
\end{equation}

Finally, the total loss is calculated as follows:
\begin{equation}
    L = L_{text} + {\lambda}L_{image}
\end{equation}

\section{Experimental Setup}

\subsection{Dataset}
There is currently one dataset \cite{11zhu2018msmo} for MSMO tasks. However, this dataset is marked only on the test set due to the unsupervised image selection method. It is not suitable for our HCSCL, which needs the image objects information and annotating the most relevant image. Therefore, we construct a large-scale Chinese summarization dataset with complete image annotations and fine-grained object information. Specifically, documents and summaries are selected from TTNews \cite{hua2017overview} and THUCNews \cite{maosongsun2016thuctc}, including sports, entertainment, current politics, society, technology, etc. For each selected document, we search it in Baidu Image Searcher\footnote{https://image.baidu.com/}, and then about ten images with their captions are crawled from the website. Next, we delete noisy images such as gifs, thumbnails, and advertisements.

Image annotation from large amounts of data and images is a time-consuming and laborious task. Since the images we crawled have image captions, we use automatic selection and manual selection methods for image annotation. A Bert \cite{devlin2018bert} and ESIM \cite{chen2016enhanced} semantic matching model pretrained on LCQMC corpus \cite{liu2018lcqmc} is used to infer the relevance of article summary and image caption. Then, for automatic selection, we select the three images with the highest relevance in each document and discard the remaining images. For manual selection, five volunteers are employed to select one of the three images as the most relevant image. In addition, for each image, a Faster R-CNN feature extractor \cite{2017Bottom} is applied to extract the object features, bounding box locations, and attribute classes. Then, the bounding box locations are used to calculate the IOU between objects to generate the adjacency matrices of relation graphs. Finally, a multimodal summarization dataset with complete image annotations and image object features is constructed, including 52656 for training, 5154 for validation, and 5070 for testing. More details about our dataset is illustrated in Table \ref{table:dataset}.

\begin{table}[]
\resizebox{80mm}{15mm}{
\centering
\begin{tabular}{cccc}
\toprule
                                & Train  & Valid  & Test   \\ \hline
Num. Documents                  & 52656  & 5154   & 5070   \\
Avg. Num. Words in Article     & 953.72 & 956.29 & 970.21 \\
Avg. Num. Sentences in Article  & 19.52  & 19.57  & 19.86  \\
Avg. Num. Words in Summary     & 36.66  & 36.33  & 36.36  \\
Avg. Num. Words in Caption     & 25.07  & 25.10  & 25.10  \\
Avg. Num. Objects in Image      & 7.25   & 7.16   & 7.32   \\
Avg. Num. Scene Graphs in Image & 4.74   & 4.70   & 4.80   \\ \bottomrule
\end{tabular}
}
\caption{Corpus statistics of our dataset.}
\label{table:dataset}
\end{table}

\begin{table*}[!hpt]
\centering
\resizebox{155mm}{32mm}
{
\begin{tabular}{llllllllll}

\toprule
\multicolumn{1}{c}{} &
  \multicolumn{1}{c}{R-1} &
  \multicolumn{1}{c}{R-2} &
  \multicolumn{1}{c}{R-L} &
  \multicolumn{1}{c}{B-1} &
  \multicolumn{1}{c}{B-2} &
  \multicolumn{1}{c}{B-3} &
  \multicolumn{1}{c}{B-4} &
  \multicolumn{1}{c}{IP}\\

\hline
\textit{Traditional Textual Model}      & & & & & & & \\
PG \cite{see2017get}   & 42.54 & 28.25 & 39.95 & 38.26 & 30.78 & 24.55 & 19.92 & -  \\
S2S \cite{luong2015effective} & 30.13 & 14.40 & 28.61 & 26.36 & 17.55 & 11.07 & 7.44 & - \\
TextRank \cite{mihalcea2004textrank}        & 22.22 & 10.39 & 18.49 & 28.32 & 8.48 & 4.47 & 2.78 & - \\

\hline
\textit{Multimodal Summarization Model}       & & & & & & & \\
MAtt \cite{12li2018multi}          & 41.30 & 24.70 & 37.55 & 37.41 & 28.44 & 21.13 & 16.02 & -  \\
HOW2 \cite{4palaskar2019multimodal}           & 39.53 & 20.28 & 35.55 & 35.44 & 24.42 & 17.81 & 13.71 & -  \\
MSE \cite{9li2020multimodal}     & 42.99 & 28.78 & \textbf{41.79} & 39.02 & 31.51 & 25.21 & 20.50 & -  \\
\hline
\textit{Multimodal Summarization Output Model}      & & & & & & & \\
MSMO \cite{11zhu2018msmo}            & 42.89 & 28.25 & 39.86 & 39.06 & 31.26 & 24.78 & 19.93 & 33.50 \\
MOF \cite{10zhu2020multimodal}           & 42.41 & 28.10 & 39.60 & 38.04 & 30.55 & 24.38 & 19.71 & 33.16 \\
VMSMO \cite{6li2020vmsmo}           & 42.68 & 28.35 & 41.34 & 38.75 & 31.20 & 24.75 & 19.99 & 32.86 \\
\hline
\textit{our models}     & & & & & & & \\
HCSCL text output only            & 42.11 & 27.57 & 39.52 & 37.90 & 30.26 & 23.84 & 19.13 & - \\
HCSCL multimodal output          & \textbf{43.64} & \textbf{29.00} & 40.94 & \textbf{39.64} & \textbf{31.91} & \textbf{25.40} & \textbf{20.54} & \textbf{40.98} \\
\bottomrule
\end{tabular}
}
\caption{Rouge, BLEU and IP scores comparison with summarization baselines.}
\label{table:1}
\end{table*}

\subsection{Baseline models}
We compare our model in three categories of baselines, a total of nine models.\par

\textit{{Multimodal Summarization Output Models. }}\noindent\textbf{MSMO} \cite{11zhu2018msmo} is the first model on multimodal summarization with multimodal output task, which applies attention to text and images during generating textual summary and uses coverage mechanism to select image. \textbf{MOF} \cite{10zhu2020multimodal} is the model based on MSMO, which considers image accuracy as another loss. \textbf{VMSMO} \cite{6li2020vmsmo} is a video-based news summarization model, which proposes a Dual-Interaction-based Mutilmodal Summarizer. \par

\textit{Multimodal Summarization Models. }\noindent\textbf{MAtt} \cite{12li2018multi} is an attention-based model, which utilizes image filtering to select visual content to enhance sentence features. \textbf{HOW2} \cite{4palaskar2019multimodal} the first model proposed to generate a textual summary by exploiting video content. \textbf{MSE} \cite{9li2020multimodal} is a model which focuses on enhancing the ability of the encoder to identify highlights of the news.\par

\textit{Traditional Textual Models. }\noindent\textbf{PG} \cite{see2017get} is a sequence-to-sequence framework combined with attention mechanism and pointer network. \textbf{S2S} \cite{luong2015effective} is a standard sequence-to-sequence architecture using a RNN encoder-decoder with a global attention mechanism. \textbf{TextRank} \cite{mihalcea2004textrank} is a graph-based extraction summarizer which represents sentences as nodes and uses edges to weight similarity.\par

\subsection{Evaluation Metrics}
As a widely used evaluation metric in text summarization, ROUGE (\citealp{lin2004rouge}) and BLEU (\citealp{papineni2002bleu}) are applied to evaluate the quality of the generated text summary. Besides, image precision (IP) is used to evaluate the quality of the output image (\citealp{11zhu2018msmo}), which is as follows:
\begin{equation}
    IP = \frac{1}{N}\sum\limits_{i}I(ann^{img}_i=rec^{img}_i)
\end{equation}
where $ann^{img}$ and $rec^{img}$ denote the annotated image and the output image respectively.

\subsection{Implementation Detail}
The experiments are conducted in Pytorch on NVIDIA Tesla V100 GPUs. We freeze the basic version of pre-trained Bert-base-Chinese \cite{2018BERT} for the original text embedding, which has 12 layers, 12 heads, 768 hidden dimensions, and 110M parameters. The Faster R-CNN feature extractor \cite{2017Bottom} is used for image object detection, and objects with confidence greater than 0.55 are selected. Each object has a 2048-dimensional representation. The IOU threshold is set to 0.2. To train the model, we employ Adam \cite{2014Adam} as the optimizer, and the batch size is set at 16. The initial learning rate is set to $5e^{-4}$ and multiply by 0.8 every six epochs.
\section{Results and Analysis}

\subsection{Overall Performance}
To validate the effectiveness of our model, we compare our model with three kinds of baselines: Traditional Textual Model, Multimodal Summarization model, and Multimodal Summarization Output Model. Table \ref{table:1} shows the result of comparative models on the dataset. We can obtain several conclusions from this table.
First, it shows that HCSCL achieves state-of-the-art performance in almost all evaluation metrics. HCSCL outperforms baselines 1.51\%, 0.76\% in terms of Rouge-1, Rouge-2 and 1.48\%, 1.27\%, 0.75\%, 0.20\% in terms of BLEU-1, BLEU-2, BLEU-3, BLEU-4 and 22.32\% in IP. It demonstrates the superiority of the hierarchical cross-modality correlation learning model. By exploiting the intra-modality correlation learning and the inter-modality feature aligning, the visual content can reinforce a specific part of the representation of text content, and the text content can reinforce to select the relevant image.
Second, in Rouge-L, HCSCL (40.94) is slightly worse than the text output model MSE (41.79). After analyzing the cases, we find that the text outputs of HCSCL are more relevant to image semantics. Therefore, the longest common subsequence (R-L) is less matched with the text description, affecting this metric's evaluation.

\subsection{Ablations Study}
Four comparative experiments are designed to verify the effectiveness of word-object fusion, sentence-scene fusion, and hierarchical structure. The result is shown in Table \ref{table:2}. We can see that with the hierarchical structure, the model has better performance. With the word-object fusion, the R-1, R-2, and B-1 are higher. With the sentence-scene fusion, the image selection metric IP is higher. However, when skipping the low-level word-object fusion module and using the sentence-scene fusion module directly, the model's performance worsens text evaluation metrics. It demonstrates that the low-level semantic correlation helps learn the high-level semantics and improve the summarization quality.

\begin{table}
\resizebox{84mm}{!}{
\begin{tabular}{lllllll}
\toprule
\multicolumn{1}{c}{} &
  \multicolumn{1}{c}{R-1} &
  \multicolumn{1}{c}{R-2} &
  \multicolumn{1}{c}{B-1} &
  \multicolumn{1}{c}{IP}\\
\hline
HCSCL Word-Object only   & 41.70  & 25.37 & 38.01  & 32.31    \\
HCSCL Sentence-Scene only & 38.99  & 20.41  & 35.58  & 33.36  \\
\hline
HCSCL \emph{w/o} Sentence-Scene Fusion          & 41.05 & 24.04  & 37.44  & 31.94   \\
HCSCL \emph{w/o} Word-Object Fusion           & 38.35  & 19.94  & 34.97  & 30.83   \\
\hline
HCSCL        & 43.64  & 29.00   & 39.64  & 40.98  \\
\bottomrule
\end{tabular}
}
\caption{The ablation results for HCSCL. "Word-Object only" means a model only contains Text Encoder, Image Encoder, and Word-Object Fusion. "Sentence-Scene only" means a model only contains Sentence Encoder, Image Graph Encoder, and Sentence-Scene Fusion.}
\label{table:2}
\end{table}

\subsection{Fine-grained Semantic Diversity Analysis}

To test fine-grained degree \cite{yang2020hierarchical}, we calculate the number of named entities and relations in the output text summaries. The pre-trained relation extraction model NEZHA \cite{wei2019nezha} is trained on the LIC2021 Competition dataset\footnote{https://aistudio.baidu.com/aistudio/competition/detail/65}, which extracts 24 types of name entities and 42 types of relations in our dataset. Then, the text summaries are input to the relation extraction model. Next, 24 name entities are divided into five categories: PER, ORG, LOC, WORK and OTHER, and 42 relations are divided into six categories: P-P, O-O, P-O, O-P, P-ATTR, and O-ATTR.
The statistic result of the experiment is shown in Figure \ref{fig:relation}. Note that when the ROUGE is similar or slightly lower (as shown in Table \ref{table:1}), PGN can extract more name entities and relations than the other multimodal summarization models, which shows that the summaries generated by the previous multimodal summarization models have a certain loss of semantic information. Compared with the baselines, the summaries generated by HCSCL have the largest number of named entities and relations. From these results, we find that HCSCL can generate summaries with the richest semantic information. This is because HCSCL learns the correlation between the objects in the multimodal data, which is more effective in discovering the relationships and entities even if some critical information is missed in the text data.

\begin{figure}[t]
    \begin{center}
    \includegraphics[scale=0.50]{img/figure3_v2.pdf}
    \end{center}
    \caption{The histogram statistics of the number of named entities and the relations in output text summary.}
    \label{fig:relation}
\end{figure}

\begin{table}[]
\resizebox{84mm}{6mm}{
\begin{tabular}{ccccccccc}
\toprule
              & HCSCL & PGN & MSMO & MSE & HOW2 & MAtt & VMSMO & S2S \\ \hline
Ent. & 4006 & 3972 & 3844 & 3778 & 3746 & 3691 & 3566 & 3489 \\ \hline
Rel.      & 1911 & 1900 & 1836 & 1814 & 1801 & 1764 & 1702 & 1643 \\ \bottomrule
\end{tabular}
}
\caption{The total number of name entities and relations in output text summaries.}
\end{table}

\begin{figure}[!htpb]
    \begin{center}
    \includegraphics[scale=0.34]{img/figure4_v4.pdf}
    \end{center}
    \caption{The pie charts for human evaluation.}
    \label{fig:human evaluation}
\end{figure}

\subsection{Human Evaluation}
To further evaluate our model's performance, 100 multimodal summaries generated by MSMO, MOF, VMSMO, and HCSCL are selected for human evaluation. Then five graduate students are volunteered to choose the most Diverse, Coherent, and Relevant (how the image matches the textual summary). The evaluation is shown in Figure \ref{fig:human evaluation}. Compared with MSMO, MOF, and VMSMO, the multimodal outputs generated by HCSCL are the most diverse, coherent, and relevant, obtaining 39.8\%, 37.8\%, and 50.8\% votes. It further demonstrates that HCSRF can learn the most representative and abundant semantic information from the article.

\section{Conclusion}
In this paper, we propose a Hierarchical Cross-Modality Semantic Correlation Learning Model for multimodal summarization, which exploits the intra- and inter-modality correlation in the multimodal data to learn the summary information from both modalities complementarily. The experimental results on the well-designed dataset show that our model can generate the most diverse and coherent text summary with the most relevant image. The novelty of this work is to tackle the multimodal summarization by proposing a multimodal model to learn the heterogeneous structure of different modalities, and hence the correlation between them. This complements the current research, which is not effective to mine the latent and important information inside the multimodal content.

redpajama_set_name: RedPajamaArXiv


Donna DeMari bought her first camera at the age of eighteen after signing up for a photojournalism course in college. At nineteen she was introduced to Oliviero Toscani, the Italian photographer and art director, who noticed her talent and encouraged her to shoot fashion. DeMari was soon working for prestigious magazines and photographing supermodels, including Kate Moss, Tyra Banks, Heidi Klum, Naomi Campbell, Christy Turlington, and Helena Christensen. She became the first woman to shoot the Sports Illustrated Swimsuit Edition, shooting in Bali and Hong Kong in 1995 and Costa Rica in 1996.
But throughout her years as a fashion photographer, DeMari also always took pictures of horses, wherever and whenever she encountered them. Ralph Lauren discovered her equine images, and they have been decorating the windows and walls of Ralph Lauren stores around the world ever since.
But all these amazing accomplishments and "pinnacle" experiences have not yet fulfilled Donna DeMari—she is still seeking…and photographing horses in the most unexpected ways. Recently she has journeyed to India, immersing herself in its exotic mystery and absorbing its depths, its people, and its horses. This rich and rewarding experience has so impacted her, she desires nothing more than to share it with others.
If you are searching for something to move you; if you love capturing the vibrations of life on camera; and if you are transfixed by the horse and all he represents, then this is your journey.
RIDING BARRANCA is available from the TSB online bookstore, where shipping in the US is FREE.
Today, we are remembering those we lost.
Perhaps because of the solemnity of the day, September eleventh, the air seems particularly still, as blue and clear as it was nine years ago when the world was left in shock by the attacks on the World Trade Center.
I saddle up Barranca and decide to take him out alone, heading along the top of the ridge, planning to take a new path down toward Round Pond. Riding through the sarsaparillas, a tunnel of grey opens to a chartreuse splash at the end. Soon, the new path disappears into unmarked woodland, but we continue bushwhacking along. No one has been down here in a long time, and there are lots of branches to break.
We reach a treacherous slide of rocks, but with a little urging, Barranca makes it over. I keep expecting to spot a glimpse of the pond, but all I see is palomino-colored bracken, the magnificent forest dressed up in green and gold. Finally, I spot a bit of blue through the leaves and know we are almost there. I feel a definite thrill, riding this new trail for the first time. I'm inside the moment, and Barranca is all fired up. When we hit the dirt road at water level, we canter to the end of the lake where there is a manmade dam. Standing there, looking out over the pond, I hear someone shooting a gun, target practice, getting ready for hunting season, no doubt, and it is disturbing. Guns, ammunition, explosions, crashes, towers collapsing—why is there so much destruction when peace can surround us?
By the time I get home, Barranca is covered with pine needles. As my feet hit the solid earth, I feel grounded, as if I have somehow absorbed my horse's sure-footedness and a powerful surge of energy moves through me, passing into my core.
To read more from RIDING BARRANCA by Laura Chester, CLICK HERE.
The following review of Laura Chester's new book RIDING BARRANCA is by acclaimed poet and author Lyn Lifshin. Her many books include several about racehorses, including The Licorice Daughter: My Year with Ruffian; Barbaro:Beyond Brokenness; and coming in 2014, Secretariat: The Red Freak, the Miracle, all from Texas Review Press. Also forthcoming is a chapbook, Lost in the Fog from Finishing Line Press.
As soon as I saw the stunning, breathtaking cover of Laura Chester's Riding Barranca, a startlingly beautiful image of horse and woman moving through rippling water, moving as one, mirroring the magical ride that's about to unfold, I felt I was in for an amazing journey.
In another section she writes, "When my parents were passing through New York and I wanted to have a meal with them—my mother's response was, 'This is our time to be alone together, Laura. We're only seeing the people we really want to see.' " When she asks to stay in the pink room, her mother responds, "But that 's a nice room…" Like a Greek chorus, her words move the reader though years of intense landscapes, both inner and outer, sometimes to familiar and almost forbidden places, scary vistas she can't help but come back from changed. The calmer landscapes with "the scent of juniper and sweet fern that smells mildly like peppermint when crushed by the horses' hooves," and the still painful memories move like dancers on a dance floor—first one leads, then the other.
I didn't want this book to end which is always a good sign that I am in the presence of intensely beautiful writing and magic. A cremation on the banks of the Ganges takes her back to the cremation of her mother in Wisconsin. A risky horse ride in India reminds her of a time when her father rode into a bull pasture with a bull whip in hand while she was on a pony, a trigger for anxiety. On Barranca, with the calmness and ease of a good horse, she sees things she hasn't before. She has also learned to say, "No, I do not want to do this." Through landscapes, often silent and refreshing, sacred as a church, family history reshapes itself like pieces of glass in a kaleidoscope, always there but seen at a different angle.
I can't imagine anyone not fascinated by the connection between humans and animals that Chester explores. Dogs and cats, not just horses, can soothe, console and connect. Anyone close to an animal knows these creatures merge with the history of one's life and bring emotions that are rare and powerful.
The mother and daughter relationship, central to Chester's year of watching her mother go through the last stages of Alzheimer's disease, is revealed in some of the most moving passages—extremely intense, often painful, heartbreaking and raw, yet singing as they must have years before. Having edited Tangled Vines, a collection of mother and daughter poems, I was not surprised how powerful Chester's writing is about this relationship: intense, ambivalent, passionate, difficult, fierce, never easy or simple, a relationship that never ends, not even with death.
At the heart of the best connection, there is always some darkness, envy, ambivalence, combined with a longing attempt to win the mother's love, approval. Riding her beautiful, beloved Missouri Foxtrotter, Barranca, Chester takes us deep into the forest of many family relationships with their estrangements, reconciliations that no family is without—betrayals, rage, sickness and death, as well as support and comfort.
She leaves us with what she's learned: "Riding Barranca puts me in the moment, which is where I want to live." This is a beautiful, special book. It will touch everyone who reads it. I couldn't recommend it more.
RIDING BARRANCA is available from the TSB online bookstore.


text:
James Frenkel
broadway_jimmy
The Ugly Woman of Castello di Putti
A.M. Dellamonica
Wed Mar 5, 2014 10:00am
Edited by: James Frenkel
Returning to the world of Stormwrack where she set the Tor.com story "Among the Silvering Herd," A.M. Dellamonica offers a new story that takes us deeper into this fascinating world, the site of her new fantasy novel Child of a Hidden Sea. The Fleet, integral to the governing of a world that is mostly water sprinkled with a number of islands, must deal with a unique form of magic, Inscribing, which is so subtle that its effects can sometimes only be known in retrospect. When a ship of the fleet visits an island where scripping is common, the crew members of the sailing vessel Nightjar are at a disadvantage when faced with local matters of which they know little or nothing at all. Strangers on the shore, indeed, they may enjoy the local customs . . . but also may attract unwanted attention that could cost them more than embarrassment or money.
The Price Of Doing Business
DB Jackson
Wed Feb 19, 2014 10:00am
Ethan Kaille is a Thieftaker in Boston in the years leading up to the American Revolution. Having suffered losses and reversals in his life, he is neither naive nor without considerable personal resources. He isn't just a detective; he's also a conjurer, which makes him someone who lives on the margins of polite society. Some people fear his powers; others merely find him a distasteful rogue who should simply go away . . . but still, he is useful to the powers-that-be when problems arise requiring his unusual skill-set. In the novels Thieftaker and Thieves' Quarry, as well as in the story "A Spell of Vengeance," which was published on Tor.com, Kaille has had to deal with a variety of crimes and their perpetrators. The story that follows is one from Kaille's early days as a thieftaker in Boston. In it, he must face a formidable foe, one of a most unexpected sort, whose own powers, very different from his own, prove the equal of Ethan's.
Nighttime in Caeli-Amur
Rjurik Davidson
Wed Jan 15, 2014 10:00am
Caeli-Amur is a city-state where magic and technology are interchangeable; where minotaurs and sirens are real; where philosopher-assassins and seditionists are not the most dangerous elements in a city alive with threat. During the day, the ordinary citizens do what they must to get along. But at night, the spirit of the ancient city comes alive, to haunt the old places.
Wed Nov 7, 2012 10:00am
Ah, love. A many splendored thing. Here is a rather unusual love story, sweet and strange as could only happen in the post-magical reality of the Indigo Springs "event." Blue Magic, the sequel to Indigo Springs was published in April and here's a tasty tale from that world to tickle a reader's fancy.
A Spell of Vengeance
Wed Jun 13, 2012 10:00am
Ethan Kaille is a thieftaker in Colonial Boston, scratching out a living by restoring stolen property to its rightful owners. But unlike others in his profession, Ethan relies on magical spells as well as his wits to track down thieves. Being a conjurer doesn't make him popular with the law in Boston, so Ethan is taken aback when the sheriff seeks his help in settling a dispute between a pair of wealthy merchants and a ship's captain who has threatened their lives. Ethan knows the captain can back up his threats with magic of his own. But there is more to this matter than the merchants have let on, and Ethan soon discovers that what he doesn't know might actually kill him. D. B. Jackson's first Ethan Kaille novel, Thieftaker, will be published on July 3, 2012.
Among the Silvering Herd
Read Among the Silvering Herd, a Tor.com original fantasy story by A.M. Dellamonica
The Battle of the Round
Peter Orullian
Tue Apr 12, 2011 9:30am
Battle of the Round by Peter Orullian
Sacrifice of the First Sheason
Wed Nov 3, 2010 9:30am
Palamon was part of the collective that formed the world, made its mountains, its people, its rules. When the fledgling world is threatened, only he will do whatever it takes to save it.
Emmet Asher-Perrin Terry Pratchett Book Club: Wyrd Sisters, Part V 2 days ago
Chris on Classic Science Fiction Tearjerkers 23 mins ago
Lisamarie on WandaVision Plays With Reality and Sitcom History in Its Two-Part Premiere 28 mins ago
James Davis Nicoll on Five Thrilling SF Stories About Patrolling Space 42 mins ago
Kathleen E. King on Five Thrilling SF Stories About Patrolling Space 55 mins ago
garreth on Star Trek: Voyager Rewatch: "Deadlock" 1 hour ago
Gorgeous Gary on Jo Walton's Reading List: December 2020 1 hour ago
AeronaGreenjoy on Terry Pratchett Book Club: Wyrd Sisters, Part V 3 hours ago

redpajama_set_name: RedPajamaCommonCrawl


text:
package org.apereo.cas.authentication.bypass;

import org.apereo.cas.authentication.CoreAuthenticationTestUtils;
import org.apereo.cas.authentication.credential.BasicIdentifiableCredential;
import org.apereo.cas.authentication.credential.UsernamePasswordCredential;
import org.apereo.cas.authentication.mfa.TestMultifactorAuthenticationProvider;
import org.apereo.cas.configuration.model.support.mfa.MultifactorAuthenticationProviderBypassProperties;
import org.apereo.cas.services.DefaultRegisteredServiceMultifactorPolicy;

import lombok.val;
import org.junit.jupiter.api.Tag;
import org.junit.jupiter.api.Test;
import org.springframework.context.support.StaticApplicationContext;
import org.springframework.mock.web.MockHttpServletRequest;

import java.util.List;
import java.util.Map;

import static org.junit.jupiter.api.Assertions.*;
import static org.mockito.Mockito.*;

/**
 * This is {@link CredentialMultifactorAuthenticationProviderBypassEvaluatorTests}.
 *
 * @author Misagh Moayyed
 * @since 6.2.0
 */

@Tag("MFATrigger")
public class CredentialMultifactorAuthenticationProviderBypassEvaluatorTests {
    @Test
    public void verifyOperation() {
        val applicationContext = new StaticApplicationContext();
        applicationContext.refresh();

        val provider = TestMultifactorAuthenticationProvider.registerProviderIntoApplicationContext(applicationContext);

        val eval = new DefaultChainingMultifactorAuthenticationBypassProvider();
        val bypassProps = new MultifactorAuthenticationProviderBypassProperties();
        bypassProps.setCredentialClassType(UsernamePasswordCredential.class.getName());
        eval.addMultifactorAuthenticationProviderBypassEvaluator(
            new CredentialMultifactorAuthenticationProviderBypassEvaluator(bypassProps, TestMultifactorAuthenticationProvider.ID));

        val principal = CoreAuthenticationTestUtils.getPrincipal(Map.of("cn", List.of("example")));
        val authentication = CoreAuthenticationTestUtils.getAuthentication(principal);
        val registeredService = CoreAuthenticationTestUtils.getRegisteredService();
        val policy = new DefaultRegisteredServiceMultifactorPolicy();
        when(registeredService.getMultifactorAuthenticationPolicy()).thenReturn(policy);
        assertFalse(eval.shouldMultifactorAuthenticationProviderExecute(authentication, registeredService, provider, new MockHttpServletRequest()));

        bypassProps.setCredentialClassType(BasicIdentifiableCredential.class.getName());
        assertTrue(eval.shouldMultifactorAuthenticationProviderExecute(authentication, registeredService, provider, new MockHttpServletRequest()));

    }
}

redpajama_set_name: RedPajamaGithub


Home / FAQ / Can I customize the graphics?
Can I customize the graphics?
Of course, you can choose from our graphics or send us your favorite, we will adapt to it, customization is included in the price.
Next post: Are the software and tables included?


text:
Hugh Grant | Junior Achievement of Greater St. Louis
Hugh Grant has worked in agriculture his entire career, starting with a part-time job picking lettuce and tomatoes in the countryside of Scotland, to leading Monsanto Company as chairman and CEO for the past 13 years. As the first member of his family to attend college, Mr. Grant discovered a passion for science while studying agricultural zoology and molecular biology at Glasgow University. He recognized the potential for innovation to transform agriculture and went on to earn an advanced degree in agriculture at Edinburgh University and an MBA at the international Management Center in Buckingham. In 1981 he responded to a help-wanted ad and accepted his first position with Monsanto, developing the company's products with farmers across Scotland.
Over the last thirty-five years, Mr. Grant has worked for Monsanto on three continents, managing key elements of the global business and helping to diversify the company's products and technology solutions. Today, as chairman and CEO, he leads more than 20,000 employees in 67 countries, with annual revenues exceeding $15 billion. Under his leadership, Monsanto works to provide a broad range of solutions to help farmers nourish a growing world. The company is also focused on developing and advancing sustainable agricultural practices to help conserve the planet's natural resources. And in 2015 he spearheaded Monsanto's unprecedented industry commitment to make their operations carbon neutral by 2020.
During Mr. Grant's tenure, Monsanto has been named to Fortune's World's Most Admired companies multiple times, and the company is routinely recognized by national and global organizations for employee innovation and workplace satisfaction. He has also been recognized on Barron's list of Most Respected CEOs, Harvard Business Review's Top 50 Best Performing CEOs and as "CEO of the Year" by Chief Executive Magazine. In 2011 he was inducted as a member of the American Academy of Arts and Sciences.
Hugh Grant and his family have made St. Louis their home for more than twenty years, and he is deeply committed to the progress and prosperity of the regional community. He is an active member of Civic Progress in St. Louis and serves on the board of trustees for Washington University.


The Finksburg Post Office is located in the state of Maryland within Carroll County. This location serves 10,650 Finksburg residents with a median income of $93,731. It's estimated that approximately 16,999 packages pass through this post office each year.


By the end of the 1920s 60 of cars and 80 of radios were bought on installment credit(end note 16). For an economy to function properly, total..
Department of English, The Chinese University of Hong Kong). Course Details, course Code : eltu3413, unit :. Specific language features which are commonly seen in thesis introductions..
the Field of Library and Information Science. Location The location generally refers to the place where the readers can find the source. Was the source found on social media, such as a tweet, Reddit, or Instagram post? If your research focuses on a specific individual, you can begin the citation with that individuals name (in reverse order) and their role. Need help creating your in-text or parenthetical citations? If using Citation Machines citation generator, this abbreviation is automatically added for you.
For page numbers, use the abbreviation. Search our giant database of original essays classified by topic Stuck on your essay? Should you be using a footnote in the place of an in-text citation, it is necessary to include the surname of the writer or editor, along with the title of the work, edition, series, location of publication, date of publication and the name of the. Follow it with the title of the full source, in italics, and then add a comma. Include the publisher for any source type except for websites when the name of the publisher is the same as the name of the website. Said, imperialism is defined by the practice, the theory, and the attitudes of a dominating metropolitan center ruling a distant territory ( 9 ). Student Choice in the Undergraduate Curriculum: Student-Selected Components. Tulsa Studies in Women's Literature, vol. The ability to use a screen name or username in place of an authors full name. Name the page Works Cited, unless your list only includes one citation. If it was published independently, italicize it, followed by a period. If your research project focuses on an additional individual besides the author, or you feel as though including other contributors will help the reader locate the source themselves, include their names in the citation.


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Biden rescinds Global Gag Rule
Photo by Jon Tyson
Newly appointed US President Joe Biden cleans house after 4 years of the Trump administration. The US has rejoined the Paris Climate Agreement, will not withdraw from the World Health Organisation and will again fund UNFPA, the United Nations Population Agency.
On top of all these executive orders, Biden also repealed the Mexico City Policy or Global Gag Rule. This policy, which prohibits US aid to foreign organisations that refer people to centres that offer abortion or discuss abortion as an option, was successively instituted by Republican presidents and repealed by Democratic presidents. An on and off relationship for years, with all the consequences on the ground.
United States undermine women's rights in latest UN resolution on sexual violence
The UN Security Council adopted a weak resolution on the uptake of sexual violence in conflict. The Americans insisted on deleting any reference to sexual and reproductive health care for victims of rape and sexual violence from the text.
The Trump administration is systematically undermining the consensus on sexual and reproductive health and rights. The administration wants to prevent any possible reference to abortion. With his entry into office the administration reintroduced the Mexico City Policy which prohibits American development aid to organisations which provide information on abortion or referrals to other organisations, even where abortion is allowed by law.
Parliament calls on government to step-up the promotion of gender equality in Belgium's development cooperation
On March 15 the Belgian Chamber of People's Representatives adopted a resolution to promote gender equality and women's emancipation in the implementation of the 2030 Agenda for sustainable development. The resolution, an initiative by MP Fatma Phelivan, calls on the Belgian government to step-up it's efforts.
Afri
Amnesty Chair
Belgian Development Cooperation
Belgiu
Educaid
European Development Days
Flag System
FP2020 Summit
Funding Gap
Gender Action Plan
Genderbased Violence
Hiv Treatment
Hiv Vaccine
LSHTM
Marleen Temmerman
Maternal Morbidity
Parliamentarians For The 2030 Agenda
Parliamentary Delegation
Perspective 2030
Policy Note
RMNCH
Sensoa Flagsystem
Sexual And Reproductive Health And Rights
She Decdes
SRHR
Teenage Pregnancies
WAS 2017
Worlds AIDS Day


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Launching COVINFORM, a project analysing the impact of COVID-19 governmental response on vulnerable groups
Since its outbreak in Europe in February 2020, the COVID-19 pandemic has had an unprecedented impact on societies. Vulnerable groups have been impacted disproportionately: even in countries with well-developed responses, the outbreak and its repercussions imperil the basic well-being of social groups whose livelihoods are already precarious, while the uneven distribution of suffering threatens to aggravate inequality and division. COVINFORM analyses and critiques COVID-19 responses on the levels of government, public health, community, and information and communications.
On 5 and 10 November 2020, a kick-off meeting was held for a new exciting project investigating the impact of the pandemic. COVINFORM is a three-year, EU-funded research project with the goal to conduct research on COVID-19 responses on three levels:
developing risk assessment models based on available quantitative data at the European level,
analysing documentary sources on both national and local level in 15 chosen countries,
conducting primary empirical research in 10 target communities, gathering testimonials and analysing a multitude of sources to get a full picture of the effect of the governmental response to COVID-19 on individuals within the target communities
During all research activities, COVINFORM will take into account the intersectional nature of health and socioeconomic vulnerabilities.
"We are currently living through a global health crisis that has and is still impacting all of us. At the same time, this crisis has not affected everyone in the same way. We could observe how health and socioeconomic vulnerabilities are intersected; how certain groups have been disadvantaged compared to others by the different responses. Within the COVINFORM project, we will analyse the impact of COVID-19 responses on vulnerable groups from a variety of different perspectives. It gives us the possibility to make a difference, for this and potential upcoming crises." – said Diotima Bertel, the COVINFORM project coordinator.
Promising practices will be evaluated in target communities through case studies spanning diverse disciplines (social epidemiology, the economics of unpaid labour, the sociology of migration, etc.) and vulnerable populations (COVID-19 patients, precarious families, migrating health care workers, etc.). The project will culminate in the development of an online portal and visual toolkit for governments, healthcare, and citizens. It will integrate preliminary data, risk assessment models and case study findings to prepare empirically grounded policy and assessment guidance. The goal of the project is to support policymakers with an evidence-based approach to mitigating the effects of COVID-19 and future pandemics on vulnerable and marginalised communities.
The COVINFORM project brings together 16 partners from 11 countries who will integrate their expertise in a variety of disciplines as part of the project's multi-disciplinary approach. The consortium consists of:
A leading group of 5 academic universities who are experts in human, social and legal fields and with strong ethical and research experience.
A driving group of 6 practitioners and research organisations representing different communities (research hospitals, NGOS, research organisations, Emergency Medical Services), providing a large coverage of the EMS and acute care practitioners, decision and policy maker chain.
The participation of 4 Industry and SMEs from the medical and security sectors, bringing the innovation and commercial exploitation experience.
COVINFORM is one of 23 new research projects funded by the European Commission with a total of €128 million to address the continuing coronavirus pandemic and its effects.
Website https://www.covinform.eu/
E-Mail This email address is being protected from spambots. You need JavaScript enabled to view it.
Twitter https://twitter.com/COVINFORM_EU
You are here: Home News Latest News Launching COVINFORM, a project analysing the impact of COVID-19 governmental response on vulnerable groups


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I am not sure if you guys have heard of Maya the Bee before? Recently my daughter came across the series of Maya the Bee on Netflix. She instantly fell in love with Maya the Bee although my son is not a huge fan. I am excited to announce that Maya the Bee buzzes onto DVD and Two Disc Blu-Ray and 3d combo pack. This is available in stores everywhere on May 19, 2015 from SHOUT! FACTORY KIDS!!
If you have not heard about Maya the Bee yet here is a little background for you:
Kids and parents across North America will soon be enamored by the world famous little bee named Maya and her insect friends when the international smash hit "MAYA THE BEE MOVIE" makes its buzz-worthy debut on DVD and on two-disc BLU-RAY™ 3D Combo Pack (Blu-ray 3D and 2D, DVD and digital) from Shout! Factory Kids on May 19, 2015. The Blu-ray 3D Combo Pack allows viewers to enjoy "MAYA THE BEE MOVIE" on the platform of their choice and includes movie presentation on Blu-ray 3D and 2D, DVD and a digital copy of the movie. Fans can pre-order their copy now by visiting ShoutFactory.com
Already a great box office success in much of the world, "MAYA THE BEE MOVIE," based on the well-known children's novel and popular animated series in Europe, Australia and Asia, offers an immersive cinematic adventure into a spectacular macroscopic world. This highly anticipated animated feature brings to life an enchanting story of a little bee named Maya and her exciting adventure of self-discovery and fantastical fun. A must-have for fans of all ages, "MAYA THE BEE MOVIE" is an instant classic that delivers wholesome family entertainment— emphasizing the importance of friendship, freedom and courage. This internationally acclaimed film can also be seen in select theaters in Los Angeles and New York on May 1, 2015.
"MAYA THE BEE MOVIE" Synopsis
Maya is a little bee with a big heart. She dreams of an exciting life of freedom, fun and adventure – the world is simply far too interesting to stay inside a hive! Her funny attempts to fit in bring her into trouble with the Queen's narrow-minded royal advisor, Buzzlina. With her best friend Willy, Maya buzzes out into the meadow on an exhilarating adventure of self-discovery.
While most bees believe that other insects should be feared, Maya can't help but make friends with Flip the charismatic and eccentric grasshopper, Kurt the dung beetle and even Sting, a young hornet. But when the Queen's royal jelly is stolen, it will take Maya and all of her bug buddies to figure out who did it and how to save the day!
I would highly recommend this DVD for your children. My daughter absolutely loved this movie. It grabbed her interest and she has watched it several times. Usually it is hard for her to watch something all the way through let alone watch it twice. It was very cute movie and a great movie to watch with the family. Highly recommended.


These are all of the small groups we have going on at Cal State San Marcos.
This is a group dedicated to people who love music. Every other week we look into how different songs connect to the Bible, develop friendships, and have fun. Led by Camille.
Come as you are to experience rest and peace in God's presence through music. Every week we come together to sing and listen to music played by our very own Mario, building friendships and connecting with God. Anyone is welcome.


A donkey stone was a type of scouring block, used mostly in the mill towns of the North of England to highlight the leading edge of stone steps.
The 'donkey brand' was originally the trade mark of a Manchester company called Edward Read & Son, who were one of several makers of the stones. Other companies used other animal designs or simple lettering, but the name 'donkey stone' stuck.
Donkey stones were first used in textile mills to clean greasy steps, and give them a non-slip finish. However the stones also became popular with housewives who would use them to give doorsteps a decorative finish. Quite often the stones would be given out in exchange for old clothes or scrap metal, by rag totters, or rag and bone men as they were sometimes called.
Donkey stones were made from a mixture of pulverised stone, cement, bleach powder and water. The mixture was ground up into a thick paste and then formed into a rectangular slab on a bench. The slab was then cut up to form the individual stones. The finished stones were then placed on racks to dry, usually for several days, although sometimes the drying process would take longer if the weather was cold and damp. Donkey stones were made in three different colours; brown, using a type of sandstone called cotta stone from Northampton; white, using a type of stone from Appley Bridge quarry near Wigan, and cream, using a blend of the two.
The use of donkey stones gradually died out during the 1950s and 60s. The last manufacturer of the stones was a company called Eli Whalley, founded in the 1890s, in Ashton-under-Lyne, which ceased trading in 1979. Some of that company's old machinery is preserved at the town's Portland Basin Industrial Museum, and a blue plaque commemorates the site of the old works at Donkey Stone Wharf on the canal. Eli Whalley's stones were sold under the Lion Brand trade mark, the design of which was based on a photograph of a live specimen at Belle Vue Zoo. Another manufacturer was also based on Donkey Stone Wharf; they were called J. Meakin and Sons and made the "Pony Brand" donkey stone. They were in operation until the late 1960s.
This page was last edited on 23 March 2019, at 23:51 (UTC).


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Rosa russanovii — вид рослин з родини трояндових (Rosaceae).

Поширення
Вид зростає в центральній Азії.

Примітки

russanovii
Флора Азії
Рослини, описані 1979

redpajama_set_name: RedPajamaWikipedia


Penguin hockey is a popular sport for citizens of Penguia. The rules here are similar to that of hockey, however, instead of a netted goal, there is instead a pole that one has to hit in order to score a point. The light on the tip of the pole flashes red when this is done. Also, there are 5 members per team.
There is a Penguia Cup, where whoever wins gains a trophy.


Your wish for a cool accent piece is granted with the Daleyza genie bottle vase. Incorporating shape and symmetry, stamped metal cutout design enhances an open-and-airy aesthetic.
The vases were on sale..... Glad I brought them!!
I bought this for my new home. I have so much love for this item .
Moroccan-inspired mirror is stunning on its own, but also proves that more is better. Make a bold statement and hang a series of mirrors (each sold separately) to create a large-scale Moorish pattern. Semi-metallic finish lets the wood grain show through, adding an organic feel.


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Shaq offers to pay for funeral of teen who accidentally killed himself on Instagram Live
Posted: 9:34 AM, Apr 16, 2017
By: PIX11 Web Team
Shaniqua Stephens had just watched her 13-year-old son, Malachi Hemphill, take out the trash Monday evening when she heard the gunshot.
ATLANTA — NBA legend Shaquille O'Neill plans to cover the funeral expenses for a Georgia teenager who accidentally shot himself while his friends watched on Instagram Live.
"I heard a big boom," his mother Shaniqua Stephens said. "I couldn't tell if it was a gun shot or what."
Stephens had just watched her 13-year-old son take out the trash when she heard the gunshot.
She found her son, Malachi Hemphill, in a pool of blood. His phone was still streaming.
"We kicked in the door. We found him just laying there in a pool of blood," Stephens recounted to WXIA . "My daughter screamed and said, 'Mom turn his phone off!'."
The teen's grandmother told NBC11 that Shaq offered to pay for the funeral.
"We just broke down and started crying because Malachi didn't have any insurance," Bankston said. "We weren't prepared to bury him this young. We didn't have insurance for him. So to have that from Shaquille O'Neal it was a blessing and very touching, and we appreciate everything he do for the community."
O'Neal told NBC11 he wanted to help the family deal with the tragic death.
"No mother should have to go through this," he said. "I can only imagine the pain that she and all of Malachi's family must be feeling."


We met up with Pawel Sewera of JTB USA, specialists in travel to Japan, for tips and insight on visiting this unique country.
Q: How would you describe Japan to those considering a vacation there?
Japan is one of the most fascinating destinations in the world, a dazzling mixture of the old and new. Visitors can immerse themselves in a fascinating mix of past and present: ultra-modern technology; traditional temples; tranquil gardens and pulsing neon signs.


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Oleg Mikhaylovich Budargin (; b. November 16, 1960) was the governor of Taymyr Autonomous Okrug in Russia. He took office in February 2003 after having won elections the previous month with 70% of the vote. He was previously the mayor of Norilsk.

Budargin's term ended when Taymyr Autonomous Okrug was merged into Krasnoyarsk Krai on January 1, 2007.

References

Budargin, Oleg Mikhaylovich
Budargin, Oleg Mikhaylovich
Governors of Taymyr Autonomous Okrug
Taymyr Autonomous Okrug
People from Norilsk


Mohawk Trail State Forest - Wag!
Get your beloved fur-baby back to nature at Mohawk Trail State Forest in Massachusetts. These dense woods offer the perfect spot to recharge from the busyness of everyday life. Right through this forest flows the Cold River, where you and your canine compadre can splash about during the warmer months of the year.
The facilities here are equipped with 59 campsites, 6 of which are only accessible by foot. Be prepared to truly unplug, as no hookup options are provided in the park. A group site is available if you prefer to camp with your friends and family by your side, and 6 cabins stay open year-round for those brave enough to venture into the woods when the snow flies! Keep in mind that these structures do not allow our four-legged friends inside, so stick to summer camping if you'd like Fido to come along.
To enter the forest, you'll have to shell out $5 for a parking permit if you're local to the state and $10 if you aren't. Camping fees start at $17 for a basic site and jump to $27 if you're not from Massachusetts.
The river makes for the ideal place to spend your hot summer days whether you're going for a dip, paddling around on a kayak, or casting a line out to see what finned creature you can catch. We're sure your pupper will be thrilled to join you for a hike on one of the forest's many trails -- including a trail that was used by Native American peoples thousands of years ago (and happens to be the source of the forest's name, too!).
All doggos visiting the forest will have to abide by a few simple rules during their stay. Be sure to keep your furry friend on their leash throughout your time here and, as always, remember to clean up whatever your pup may leave behind. It's also very important to keep your barky buddy close to you at all times, as bears frequent the campground. Food lockers are provided, as are trash receptacles, so make sure you use them to keep these big, hairy forest inhabitants off of your site.
To experience a different side of Massachusetts, why not spend some time at Mohawk Trail State Forest?


Warner Bros. has formally revealed Middle-earth: Shadow of War, the sequel to Shadow of Mordor developed by Monolith Productions. The game is set to release on August 22nd for PlayStation 4, Xbox One and PC. More details and the announce trailer inside.
Burbank, Calif. – February 27, 2017 – Warner Bros. Interactive Entertainment today announced Middle-earth: Shadow of War™, the sequel to the critically acclaimed Middle-earth: Shadow of Mordor™, winner of more than 50 industry awards, including 2015 Game Developers Choice Awards' Game of the Year, Outstanding Innovation in Gaming at the 2015 D.I.C.E. Awards and the BAFTA for Game Design. Developed by Monolith Productions, Middle-earth: Shadow of War features an original story with the return of Talion and Celebrimbor, who must go behind enemy lines to forge an army and turn all of Mordor against the Dark Lord, Sauron.
Middle-earth: Shadow of War will be available for Xbox One, Project Scorpio, Windows 10 PC (Windows Store and Steam), PlayStation®4 and PlayStation®4 Pro beginning August 22, 2017. Middle-earth: Shadow of War will be available as an Xbox Play Anywhere title.
As an Xbox Play Anywhere title, players can purchase a digital version of Middle-earth: Shadow of War once and play it on both Xbox One and Windows 10 PC. Gamers can pick up where they left off on another Xbox One or Windows 10 PC, bringing all the saves, game add-ons and achievements with them at no additional cost.
For more information or to join the Middle-earth: Shadow of War conversation, visit www.ShadowofWar.com or find us on YouTube (ShadowofWarGame), Facebook (ShadowofWarGame), Twitter (@ShadowofWarGame), Instagram (ShadowofWarGame), Twitch (MonolithLive) and our Lithium Community (ShadowOfWar).
The first one was simply amazing. it was all I hoped for. the combat was everything the Arkham series started but failed to polish and build upon. I'm eagerly looking forward to this one.
For me Shadow of Mordor seemed solid but still lacking something, lots of ground to improve on. It was short, the fights were too easy (as any other Arkham-style combat brawlers), and it had only one real boss fight, the other was a stealth segment and the final boss was a quick time event. I really liked the Nemesis system and I hope they'll improve and expand it. All in all if Monolith repeats the formula and doesn't do anything stupid it should be good. After all they're a company with a good game record, they've made F.E.A.R., Condemned, Tron 2.0, Aliens versus Predator 2 and No One Lives Forever, all great games.
Although adding a token black guy for political correctness checklist is... yeah. Especially considering the source material. I believe it's the first black in LotR universe.
"waaaaah waaaaah a black guy waaaaah" Seriously this generation of closet racist gamers are friggin' annoying, spamming the comments sections everywhere with all their immature xenophobic whinings and paranoia about sjw agendas. I can't find a single gaming website now that's devoid of their constant cry me a river baby cries.
Oh, come on. It's not paranoia, diversity is something companies and people strive to nowadays. This year's Oscars for example, after 2 years of #oscarssowhite hashtag shaming on twitter Jimmy Kimmel gleefully says "I want to say thank you to president Trump, I mean, remember last year when it seemed like the Oscars were racist?" implying that Oscars not being diverse enough in the past was a racism prejudice problem.
And I'm not going digging for these examples, either, I'm just showing that it's not some /pol/ lunatics seeing conspiracies in everything, it's an actual directives and guidelines people follow. And it's okay, writers and artists should be able to put whatever they want into their creations. I'm not saying anything about Finn and Rei in new Star Wars movies because they fit and are enjoyable characters, but when diversity issue is handled so hamfisted as in Battlefield 1 or this game with no regards to source material, then it's bad.
I too was a bit baffled by the black guy. I'm pretty sure there aren't any black people in Gondor. Unless, the dude is a descendant from the Men down south.
But anyway, I don't really care much. This was just a CG announcement trailer anyway.
I never liked the character head design, Aragorn wannabe tbh hopefully we can swap or add (beard).
I also hope its not repetitive since first one became that and freaking boring, though fantastic combat.
When I heard the whole black guy thing in the comments I was a little concerned... until I watched the video. Clearly they are taking more liberties with the lore than just race of one guy. A guy bound to the spirit of an elf forging a ring of power at mount doom? Clearly not 100% true to Tolkien's fiction in any event.
That said I seem to have noticed a trend where it seems as though they are trying fill people of various genders/races etc in as many places as possible.
While I think it is important that there be more diversity in general I don't there HAS to be. For instance, it is important that there be more black people, asian people, women etc I don't think it should become a numbers thing. For instance, I don't think there HAS to be female protagonist of GTA by force. If the creators want to and had an interesting concept? Sure. Or I don't think that Lara Croft has to become a Black woman/man or any other thing just for the sake of diversity.
When I was younger I used to long deeply for more black characters in films and also felt that black people I saw in films were different from my own local culture (I am a Nigerian). At a point, it got so bad that it even became resentment against white people for "suppressing, oppressing and stealing our culture." I am glad I got away from that mindset.
That is not to say that I am not happy and glad to see black characters well rendered in various media or hear of a high quality film made by a black person. I very much am. All I am saying is that passion and creativity should be guiding force of any creative pursuit not playing to demographics.
This should replace the last sentence above. I find it more accurate and honest.
All I am saying is you don't to make your game, comic, film about fitting every ethnicity and gender in any corner possible. I personally like things to be a bit more flexible.
I spit my tea, when I saw Lifespan section. That's the dorkiest dorkiness I've ever dorkened.
Racism - prejudice, discrimination, or antagonism directed against someone of a different race based on the belief that one's own race is superior.
Not sure how not wanting Y in your X is racism. Lore notwithstanding.
I honestly believe that American white guilt and craving for media buzz are ruining artistic integrity hard. It's also the absolute worst aspect of Americanisation and Globalization. People from Finland start believing that their ancestors owned slaves and they are somehow in debt to the Middle East or Africa. It's probably funnier than anything else, though. For now.
American blacks have ruined it for all of us.
Almost forgot. I'd love to play a good action game, set in Sub-Saharan Africa. But I doubt that shoving "african-Americans" in every game will make me less racist than I already am.
White colored people not wanting dark skinned people to be represented in a setting that also has fans amongst said dark skinned people ( Yeah there are many black fans of Middle-Earth ) is racism to me. Also, you "that's a liberal agenda" folks always whine about how it's a diversity master-plan being pushed by white devs and ignoring the fact that there ARE many black video game developpers that work in this industry and who would like to have people of colors fairly represented in this medium.
I doubt that shoving "african-Americans" in every game will make me less racist than I already am.
Oh, you seem plenty racist, i don't doubt that for a second.
Less of a plan, and more of a cattle herd doing what's cool these days. With marketing teams being their shepherds.


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TCBI amends quarterly dividend for Series B preferred stock.
DALLAS — June 1, 2021 — Texas Capital Bancshares, Inc. (NASDAQ: TCBI), the parent company of Texas Capital Bank, and its board of directors declared, in a release dated April 27, 2021, a quarterly cash dividend of $14.375 per share of the 5.75% Fixed Rate Non-Cumulative Perpetual Preferred Stock, Series B (the "Series B Preferred Stock"), equivalent to $0.359375 per depositary share, each representing a 1/40th interest in a share of the Series B Preferred Stock. The dividend amount is being amended to include the additional period from March 3, 2021, the Series B Preferred Stock original issue date, to March 15, 2021 in the dividend period. The revised cash dividend is $16.2917 per share, equivalent to $0.4072917 per depositary share. The depositary shares are traded on the NASDAQ under the symbol "TCBIO". The Series B Preferred Stock dividend is payable on June 15, 2021, to holders of record at the close of business on June 1, 2021.
Texas Capital Bancshares, Inc. (NASDAQ: TCBI), a member of the Russell 2000® Index and the S&P MidCap 400®, is the parent company of Texas Capital Bank, a commercial bank that delivers highly personalized financial services to businesses and entrepreneurs. Headquartered in Dallas, the bank has full-service locations in Austin, Dallas, Fort Worth, Houston and San Antonio.


Tynllwyn Caravan and Camping Park01654 710370work Holiday, Touring & Camping ParkTynllwyn Caravan and Camping Park is situated in the picturesque Hamlet of Rhydyronen overlooking the beautiful Dysynni Valley. Only 2.5 miles from the seaside town of Tywyn, the area is a fisherman's delight with streams, rivers, lakes and sea fishing all close by. Talyllyn Narrow Gauge Railway runs alongside the park with a station only 50 yards down the lane.
The area provides much to interest young families and individuals. Beautiful, unspoilt, natural countryside to relax in, or try pony trekking. A paradise for walking and birdwatching.
Touring vans and campers are made very welcome. Facilities offered include a modern toilet block and electric hook-ups. There is also an On-Site Shop and Children's play area.
Tynllwyn Caravan and Camping Park is situated in the picturesque Hamlet of Rhydyronen overlooking the beautiful Dysynni Valley. Only 2.5 miles from the seaside town of Tywyn, the area is a fisherman's delight with streams, rivers, lakes and sea fishing all close by. Talyllyn Narrow Gauge Railway runs alongside the park with a station only 50 yards down the lane.
All the above are per night and include VAT at 17.5%.
We also have indoor and outdoor table tennis and a TV room. Children welcome from any age.
Turn off A493 onto B4405. Turn up by grass island and carry on down the country lane.
Childrens play area is well equipped. and well maintained. Showers have hand rails. The step into the toilet block is slight.


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Microsoft Thin-KIN-g Social
Share this.......
Microsoft has revealed it's take on social orientated phones, aptly naming them KIN One and Two respectively. Both of these handsets are aimed at the younger socially connected audience, older teenagers and young adults. As they've been designed specifically for one market there are a few things missing from many handsets coming to market. For instance, there is no support for apps, no expandable memory, but perhaps this is for a reason. Everything on the phones themselves sync to a cloud service which backs them up fully, which will be heavily relying on 3G service to be on par.
Taking a look at the KIN One specs:
QVGA 320×240 capacitive touch screen, multi-touch
5 megapixel camera, LED Flash
VGA video recording
4GB of non-expandable storage
Full slider QWERTY.
KIN Two specs:
HVGA 480×320 capacitive touch screen, multi-touch
720p video recording
8GB non-expandable storage
Both of these handsets are run by Nividia's Tegra, which has the ability to output smooth graphics with more efficiency and battery life, and as both of them have the Zune HD UI at first glance, it does a pretty decent job. Both phones will have support from the Zune Music and Video service allowing to download music and video directly. Whilst the social aspect means there is support of Facebook, Twitter and one would presume Myspace too. The whole social sharing feature is based around a central spot, which acts as a focal point for sharing.
The New Way to Share
The home screen of the phone is called the KIN Loop, which is always up to date and always on, showing all the things happening in someone's social world. KIN automatically brings together feeds from leading Microsoft and third-party services such as Facebook, MySpace and Twitter all in one place, making it easier to stay connected. Customers can also select their favorite people, and KIN will automatically prioritize their status updates, messages, feeds and photos. Another unique feature, the KIN Spot is a new way for people to share what's going on in their world. It lets them focus first on the people and stuff they want to share rather than the specific application they want to use. Videos, photos, text messages, Web pages, location and status updates are shared by simply dragging them to a single place on the phone called the Spot. Once all the people and content are in the Spot to share, the consumer can choose how to share, and start broadcasting.
Both KIN phones will be available in the US via Verizon Wireless from May 2010 whilst Vodafone has taken them on to distribute in Europe and particular to Germany, Italy, Spain and the United Kingdom, and will start offering them this Autumn.
Android P will veto apps that spy with camera and…
Tags: KIN OneKIN One specsKIN TwoKIN Two specs
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September 17, 2019 Food & Drink » Food and Drink
Working Lunch: Pop's Market on Grace
Expect a line and a hefty, efficient meal at this popular downtown spot.
By Karen Newton
The Italian grinder, featuring soppressata, prosciutto, pepperoni and turkey, is one of many enormous sandwiches at Pop's Market on Grace.
Let's be clear about one thing: The sandwiches at Pop's Market on Grace are gargantuan.
Gawking at one of the behemoths, I ask our server if anyone finishes so much bread and meat. She grins, admitting that it only seems to happen when someone spends hours there working or studying. "Then they'll nibble on it the rest of the day to finish it," she says.
Another time, I comment on the sheer weight of my sandwich and our server acknowledges, "Yeah, they're so big they intimidate me, so I don't even try."
Chances are, you'll want to make the effort because there's a lot to like at Pop's.
Vintage wood floors, high ceilings and an abundance of natural light supplied by the arched Grace Street windows and the Fifth Street glass make this a welcome respite no matter the weather. Sturdy wooden tables and chairs, a lounge area, shelves filled with wine for sale and shelves of children's books fill the ample space with options. Bands like Fleetwood Mac play at an easily heard volume that gives the place a convivial feel.
Major props are in order for Pop's baking staff who daily churn out fresh-baked rye, bagels, English muffins and rolls that elevate whatever filling goes on them.
A solid all-day sandwich is the Italian grinder, thick with soppressata, prosciutto, pepperoni, provolone, Parmesan and, inexplicably, turkey. On another sandwich, chunks of chicken breast in pesto get support from thick slices of mozzarella and roasted red pepper, onion and greens, but the undisputed star is the fabulous focaccia that envelops it. Ditto the outstanding crusty rye that encases what feels like a mother lode of tuna fish with lettuce, tomato and mayo.
Pop's Market on Grace has become a downtown staple for breakfast and lunch, with items like the pictured Banh Mi.
As good-looking as it is mouth-watering, a salad of roasted beets, spring mix, cucumber and onion gets a boost from spicy peanuts and an abundance of creamy blue cheese, while a seasonal salad of skin-on sliced peaches, spiced walnuts, red onions and blue cheese over greens makes for a light lunch on a sweltering day.
The only sandwich I couldn't get behind was a meatball sub because the thin, watery red sauce it sports was almost completely lacking in seasoning, not even a hint of salt. A side of overly creamy redskin potato salad also misses the mark, tasting more of mayo than potatoes.
Breakfast, served until 11 a.m., includes a veggie frittata, biscuits and gravy and egg sandwiches, plus hot and iced coffee, orange juice and mimosas.
Pro tip: If you want one of the house-baked chocolate chip cookies, stake your claim early because they rarely last beyond noon.
Pop's Market is full-on fast casual, so you'll need to order at the counter, take a number and find a table to await delivery of your food. You're lucky if, like us, your lunch is ferried to the table by owner Josh Wright's mom, who conveys welcome and bon appétit by making you feel like you're a guest in her house.
But fair warning: Unlike many restaurants, Pop's doesn't begin lunch service until 11:30, at which time the line forms immediately although it does move at a good clip.
Surprisingly, the person taking your order doesn't always know what's possible. I was a tad surprised when requesting onions be added to my tuna sandwich to be told that, "Josh doesn't add things, he likes his sandwiches served the way he designs them."
I can accept this, but considering that the tuna arrived with bits of onion in it, why wasn't that mentioned? When we ask for the Italian grinder's roll to be toasted first, it requires a consultation with two people to ascertain that it's possible.
Workability
On my visits, there's no shortage of people working or having meetings, hardly a surprise given the availability of free Wi-Fi and an abundance of tables with room for spreading out lunch and work supplies. That many people wear headphones may speak to the relatively loud volume of the overhead music, which adds to the ambiance for nonworking eaters.
Choosing to sit at the long, window-facing counter means your back is to the distraction of the dining room, although your workspace is more compact. The lounge area in the back has a love seat and upholstered chairs clustered around a faux fireplace, making it a homier place to hunker down with your laptop, as I spotted several worker bees doing on my visits.
First off, good luck finding parking downtown at lunch time, making Pop's best suited to those who live or work in the neighborhood or don't mind a walk. But once you're there, even if the line ahead of you is long, the kitchen moves efficiently. Ten to 12 minutes from ordering to food being delivered allowed plenty of time to eat and get some work accomplished.
Did I mention the sheer weight and enormity of sandwiches at Pop's? A $10-12 sandwich that's enough for two meals is a bargain in my book. Sides run from $1.25 for bagel chips to $7.50 for pâté with gherkin gremolata — a steal, I might add — and for those working lunches that require some extra motivation, mimosas cost a thrifty $5.
There's a reason that Pop's has a devoted following. Working or not, I've yet to visit and not feel glad I'd come.
Pop's Market on Grace
Tuesdays – Fridays 7:30 a.m. – 9:30 p.m.
Saturdays 9 a.m. – 3 p.m.
415 E. Grace St.
facebook.com/popsongrace
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